Pulmonary embolism (PE) is a leading cause of death in the United States if untreated. Prompt diagnosis and treatment can dramatically reduce the mortality rate and morbidity of the disease. Computed tomographic pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of PE. Interpretation of a CT scan for PE demands extensive reading efforts from a radiologist who has to visually track a large number of vessels in the lungs to detect suspected PEs. Despite the efforts, the sensitivities were reported to range from 53% to 100%. Preliminary results from the PIOPED II study indicated a sensitivity of 83% by multi-detector CTPA. Computer-aided diagnosis (CAD) can be a viable approach to improving the sensitivity and efficiency of PE detection in CTPA images, as well as reducing inter-observer variability. The overall goal of the proposed project is to develop a robust CAD system that can provide a systematic screening of PE on CTPA scans and serve as a second opinion by automatically alerting the radiologists to suspicious locations on 2D slice and 3D volume rendering display of the CTPA images. We will develop advanced computer vision techniques to enhance the characteristics of vessels, automatically extract the pulmonary vessels, reconstruct the vessel tree, detect candidate PEs, differentiate PE from normal pulmonary structures, and identify the true PEs. The techniques will be specifically designed for analysis of the complex vascular structures on CTPA images. The specific aims of this project include: (1) developing image preprocessing method to enhance vessel characteristics, (2) developing a new rolling balloon technique in combination with structure analysis to track vessels accurately, including vessels partially or completely occluded by PEs, (3) developing multi-prescreening method for the identification of suspicious PEs at different levels of artery branches, especially for PEs in small subsegmental arteries, (4) analyzing PE features for development of classification methods, (5) developing false positive reduction method based on feature analysis and fuzzy rule-based, linear, or neural network classifiers, (6) exploring performance evaluation methodology for computerized detection of PEs, and (7) performing observer ROC study to evaluate the effects of CAD on radiologists' accuracy in PE diagnosis. The relevance of this research to public health lies in the fact that there is substantial false-negative diagnosis of PEs. CAD will potentially reduce missed PEs and improve the chance of timely treatment of patients, thus reducing the mortality rate and speed up recovery from this condition. [unreadable] [unreadable]